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MULTI AGENT REINFORCEMENT LEARNING

Name: MULTI AGENT REINFORCEMENT LEARNING
Brand: MIT
SKU: 9780262049375
Price: 28218 HUF
Availability: InStock

Szerző: ALBRECHT STEFANO V

Nyelv:

Kötés: Kemény kötésű

Kiadó: MIT

Elérhetőség: Beszállítói készleten alacsony példányszámban

Küldés 11-15 napon belül

28 218 Ft

The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARLs m...

Információk a könyvről

Szerző

ALBRECHT STEFANO V

Nyelv

Angol

Kötés

Könyv - Kemény kötésű

Kiadva

2024

oldal

394

EAN

9780262049375

Enbook ID

45775744

Kiadó

MIT

Súly

760

Teljes leírás

The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARLs models, solution concepts, algorithmic ideas, technical challenges, and modern approaches.

Multi-Agent Reinforcement Learning (MARL), an area of machine learning in which a collective of agents learn to optimally interact in a shared environment, boasts a growing array of applications in modern life, from autonomous driving and multi-robot factories to automated trading and energy network management. This text provides a lucid and rigorous introduction to the models, solution concepts, algorithmic ideas, technical challenges, and modern approaches in MARL. The book first introduces the fields foundations, including basics of reinforcement learning theory and algorithms, interactive game models, different solution concepts for games, and the algorithmic ideas underpinning MARL research. It then details contemporary MARL algorithms which leverage deep learning techniques, covering ideas such as centralized training with decentralized execution, value decomposition, parameter sharing, and self-play. The book comes with its own MARL codebase written in Python, containing implementations of MARL algorithms that are self-contained and easy to read. Technical content is explained in easy-to-understand language and illustrated with extensive examples, illuminating MARL for newcomers while offering high-level insights for more advanced readers.

First textbook to introduce the foundations and applications of MARL, written by experts in the field
Integrates reinforcement learning, deep learning, and game theory
Practical focus covers considerations for running experiments and describes environments for testing MARL algorithms
Explains complex concepts in clear and simple language
Classroom-tested, accessible approach suitable for graduate students and professionals across computer science, artificial intelligence, and robotics
Resources include code and slides

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MULTI AGENT REINFORCEMENT LEARNING

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